System and method of real-time automated determination of problem interactions

ABSTRACT

The present invention allows a CEC system to automatedly, and without human intervention, identify interactions that are likely in need of supervisor intervention. The system reviews all incoming and outgoing interactions for analysis by a metadata analytics service (MAS) software module. The MAS analyzes the interactions to generate interaction metadata, which is used by an interaction analysis engine (IAE) to score the quality of the interaction. If the quality of the interaction is not sufficient, the system marks the interaction as being a problem interaction and notifies a supervisor of the interaction. This ensures the intelligent and dynamic determination of interactions that require additional assistance and assures notification to a supervisor.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 16/806,478, filed Mar. 2, 2020, which is a continuation of U.S.patent application Ser. No. 16/356,601, filed Mar. 18, 2019, thecontents of which are hereby incorporated herein by reference in theirentireties.

FIELD

The present disclosure is directed to systems and methods of automatedcomputer analysis. Specifically, automated systems and methods ofdetermining which current real-time interactions will benefit fromsupervisor attention and automatedly notifying supervisor of thoseinteractions.

BACKGROUND

In modern high-volume customer engagement centers (CEC), there are anumber of ways communication between a customer service representative(CSR) and a customer can take place. For example, communication in a CECcan take place over the telephone, through email, and text chat, amongother known communication methods. In a CEC, supervisors are typicallyassigned to monitor and assist frontline Customer ServiceRepresentatives (CSR's). Supervisors are typically experienced staffwith detailed knowledge of the business domain the CEC is dealing with.CSR's are typically less experienced and rely on supervisors to helpwith complex or difficult interactions with customers.

Supervisors within a CEC are responsible for ensuring CSRs are providinga consistently high level of customer service. Current CEC computerizedsystems help with this task by allowing supervisors to monitor CSRsinteractions with customers. Interactions can be monitored as theyoccur. For example, the supervisor can view a list of phoneconversations that are in progress and select to listen in on one or asupervisor can view a list of active text chat conversations and selectto view the chat messages being sent between the CSR and customer. Ifthe supervisor detects a problem, such as the customer becomingfrustrated or the CSR being rude then the system allows them to takeaction. For example, the supervisor may provide guidance to the CSR(unseen to the customer) helping them to resolve the issue or thesupervisor may join the conversation or replace the CSR in theconversation to resolve the issue.

However, current CEC systems rely on supervisors to manually select anddetermine which interactions to monitor. There are not as manysupervisors as there are CSRs, so it is impossible for a supervisor tomonitor every interaction. Therefore, supervisors must use theirjudgement to select interactions to monitor, guessing where theirassistance may be necessary in order to maintain a high level ofcustomer service.

The result is that supervisors miss incidents where customer service iscompromised and the supervisor could have intervened to help if they hadbeen aware. These incidents may be detected after the fact, if customerphones back to complain or the supervisor reviews an interactionretrospectively, but customer satisfaction will already have beennegatively impacted in these scenarios. Ideally a supervisor would beable to know which interactions are likely to require supervisorintervention and be able to concentrate on those interactions. There isan unmet need in the art for a system capable of automatedly notifyingsupervisors of interactions that have a greater need for monitoring andsupervision.

SUMMARY

The present invention overcomes the deficiencies in the prior CECsystems. The present invention is directed to a system and methods ofproviding supervisors with notification of current interactions that aremost likely to benefit from supervision or to automatedly assigntroubled interactions to a supervisor.

An embodiment of the present application includes a method fordetermining if an interaction is likely to require supervisorassistance. A metadata analytics service (MAS) unit receives aninteraction. The MAS analyzes the interaction using a MAS softwaremodule and generates interaction metadata for the interaction. The MASpasses the interaction metadata to an interaction analysis engine (IAE).The IAE performs an analysis of the interaction metadata using an IAEsoftware module and determines an interaction score for the interaction.The IAE compares the interaction score to an interaction qualifythreshold and if the interaction score is above the interaction qualitythreshold, the IAE passes a notice of the interaction to a supervisorrepresentative.

Another embodiment of the present application is a system fordetermining if an interaction is likely to require supervisorassistance. The system includes a processor and a non-transitorycomputer readable medium programmed with computer readable code thatupon execution by the processor causes the processor to execute theabove-mentioned method for determining interactions that are likely torequire supervisor assistance.

Another embodiment of the present application is a non-transitorycomputer readable medium programmed with computer readable code thatupon execution by a processor causes the processor to execute theabove-mentioned method for determining interactions that are likely torequire supervisor assistance.

The objects and advantages will appear more fully from the followingdetailed description made in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWING(S)

FIG. 1 depicts an exemplary embodiment of a CEC system for automatedlydetermining current interactions that are likely to benefit fromsupervision.

FIG. 2 depicts a flowchart of an exemplary embodiment of a method ofautomatedly determining current interactions that are likely to benefitfrom supervision.

FIG. 3 depicts an exemplary embodiment of a system for automatedlydetermining current interactions that are likely to benefit fromsupervision.

DETAILED DESCRIPTION OF THE DRAWING(S)

In the present description, certain terms have been used for brevity,clearness and understanding. No unnecessary limitations are to beapplied therefrom beyond the requirement of the prior art because suchterms are used for descriptive purposes only and are intended to bebroadly construed. The different systems and methods described hereinmay be used alone or in combination with other systems and methods.Various equivalents, alternatives and modifications are possible withinthe scope of the appended claims. Each limitation in the appended claimsis intended to invoke interpretation under 35 U.S.C. § 112, sixthparagraph, only if the terms “means for” or “step for” are explicitlyrecited in the respective limitation.

CEC systems allow CSRs to engage with customers in a controlled manner.By providing organized and integrated computer-based customer serviceresources and automated monitoring of incoming customer communication todetermine the quality of the customer service interaction in real time,the CEC system can allow an organization to achieve several keybenefits. These benefits will be increased through the dynamic automatedupdating of analysis rules. First, the system will ensure the dynamicintelligent determination of which customer service conversations wouldbenefit from supervisor attention. Second, the CEC system's adaptivenature allows it to adjust to new protocols and real-time determinationsof the current quality level of a customer service conversation. Third,intelligent automated identification of problem conversations willincrease both the likelihood of rapid resolution and customersatisfaction.

In order for the system to make real-time determinations ofconversations in need of supervisor assistance, the system continuallyanalyzes an ongoing conversation which consists of numerous individualinteraction instances where the interaction may be an incoming portionof the conversation from a customer or the interaction may be anoutgoing portion of the conversation from the agent. In embodiments, itis desirable for the system to analyze the interaction to createmetadata and then score the interaction according to predeterminedcriteria. This allows the system to ensure effective identification ofproblem conversations and proper notification of a supervisor. In anembodiment, it is desirable to provide the metadata for review as wellas the interaction/utterance. In another embodiment, it is desirable toallow modification of the predetermined criteria. In yet anotherembodiment, it is desirable to allow storage of the interaction and/orthe interaction metadata at various points in the process. Inembodiments, it is desirable for the system to associate savedinteractions and saved metadata with other saved interactions andmetadata where the interactions are related.

In embodiments, it is desirable for the system to determine probleminteractions based on the interaction score. In an embodiment, it isdesirable that the interaction score be cumulative based on all previousrelated interactions. In another embodiment, it is desirable that theinteraction score be unrelated to any previous related interactions. Itis desirable that the system score interactions in real-time. In thismanner the system intelligently identifies problem interactions as theyare occurring so that a supervisor can intervene as soon as possible.

FIG. 1 depicts an exemplary embodiment of CEC system 100 for automatedlymonitoring and identifying problem interactions and automatedly alertingsupervisors of the problem conversation. Conversations are numerousinteractions 120 and 152 that together form a conversation.

CEC system 100 includes at least one CEC desktop 102 used by a CSR foroutgoing interactions 152 and for receiving incoming interactions 120.The CEC desktop 102 is connected to the CEC system Interaction servers160, which includes, for example the telephone system, text transmissionsystems, chat server system, messaging server system, etc. such that theCSR can send outgoing interactions 152 and respond to incominginteractions 120 using the CEC desktop 102 either through audio, overthe telephone system for example, or through text response, over emailfor example. It should be understood that the incoming interactions 120may not be directly received by the CEC desktop 102. Incominginteractions 120 are received by the CEC system Interaction servers 160and directed through a routing system/process and/or proceed throughanalysis to determine if other interactions are related to the incominginteraction 120 prior to being passed to the CSR at the CEC desktop 102.It should be understood that the CSR may or may not respond to theincoming interaction 120 through the same channel the incominginteraction 120 was received or through a different channel. Forexample, a customer may call into the CEC system and leave a voicemessage for the CSR and the CSR may respond to the customer throughemail response, rather than by calling the customer back, provided theCSR has available the customer email.

CEC system 100 includes a metadata analytics service (MAS) unit 110having a MAS software module 111 and an optional MAS storage 112. MASunit 110 may be a processor or a combination of a processing system anda storage system. MAS unit 110 receives interactions 120 and 152 fromthe CEC system Interaction servers 160 and analyzes them using MASsoftware module 111 to generate interaction metadata 121. Interactionsmay be an incoming interaction 120 received from outside the CEC system100 or an outgoing interaction 152 received from inside the CEC system100. Optionally, MAS unit 110 may also pass a copy of the interaction120 and 152, and/or interaction metadata 121 to internal or external MASstorage 112 for permanent or temporary storage.

An interaction 120 and 152 is a single instance of a communication thatmay be part of a larger string of related interactions, wherein theinteractions are communications between an entity outside the CEC system100 and an entity inside the CEC system 100, for example, customers andCSRs, prospective customer and a supervisor, a person making an inquiryand an automated response system in the CEC system 100. Typically,incoming interactions 120 are communications received from customer, butcould be received by prospective customers, individuals with inquires,or any other entity outside of the CEC system 100. Outgoing interactions152 typically are communications from a customer service representativesent to a party outside the CEC system 100, but could be sent by asupervisor, an automated reply system, or any other way of sending acommunication from inside the CEC system 100. It should be understoodthat these are merely examples of the parties that may be involved incommunications and should not be considered limiting. As indicatedabove, the interactions 120 and 152 may include additional associateddata 154 with each interaction 120 and 152, including, but not limited,to an indication of whether the interaction 120 and 152 is part of aseries of related interactions, reference to any previously relatedinteractions, metadata created by the previous related interactions,customer identification information, and CSR identification information.It should further be understood that the MAS system may also, uponreceipt of the interaction 120 and 152, request and receive thisadditional associated data 154 from other subsystems of the CEC systemsuch as an interaction storage database or some other system serverwhere the associated data 154 is stored.

Interactions, both incoming interactions 120 and outgoing interactions152, may be audio communication such as a telephone call, a voicemessage, a video chat, or any other type of audio communication, writtencommunication such as an email, an online posting, a direct message froma customer or CSR, or any other written or audio communication. Theanalysis of incoming interactions 120 and outgoing interactions 121 mayinclude a variety of techniques such as speech analytics or textualanalytics of audio communications or textual communications betweencustomers and CSRs. The incoming interaction 120 and outgoinginteraction 152 can further include a wide variety of channels of data.These channels can include audio or textual transcripts of phone callsor web data, but can also include more discretely occurring events suchas social medial posts, purchases, returns, or warranty claims.

The analysis of incoming interactions 120 and outgoing interactions 152may include a variety of techniques such as speech analytics, textualanalytics of audio communications or textual communications betweencustomers and CSRs, sentiment analytics of audio and textualcommunications, and interaction routing analytics. The analysis mayinclude, but is not limited to, determining the sentiment for theinteraction, the conversation type of the interaction, the time elapsedsince the incoming interaction was received and a responsive outgoinginteraction has not been sent, the amount of transfers the incominginteraction has gone through, the amount of transfers the incominginteraction and all related previous incoming interactions have gonethrough, if the outgoing interaction deviates from standard scripts andthe degree to which the script is deviated, if the outgoing interactiondoes not include information that is required to be provided, anddetermining the conversation type the incoming communication involves,for example returns or ordering. MAS software module 111 may alsoidentify interactions 120 and 152 with red flag features, such asprohibited words, phrases, or expressions. Such red flag features,offensive sentiment, tone, or word choice, or grammatical or spellingerrors in interactions 120 and 152 are indicated as part of the createdinteraction metadata 121. If the interaction 120 or 152 has additionaldata associated with it, the MAS 110 may or may not consider thisadditional data when performing the analysis. Further the MAS softwaremodule 111 may have a different set of analytics for incominginteractions 120 than for outgoing interactions 152. By way ofnon-limiting example, the MAS software module 121 may determine thatmany misspelled words in an outgoing interaction 152 indicates afrustrated sentiment and create interaction metadata 121 indicating thatthe CSR's sentiment is frustrated. However, the MAS software module 111may determine that many misspelled words in an incoming interaction 120indicates no bearing on the sentiment of the interaction or indicates ahurried sentiment.

Interaction metadata 121 may include, but is not limited to, the type ofinteraction, the number of times the incoming interaction 120 has beentransferred/rerouted, the duration of time between the currentinteraction 120 or 152 and the previous related interaction, thesentiment, tone, and/or word choice of the sender, the type ofinteraction, intent and/or meaning of the interaction, presence ofthreats, and/or a list of business entities referenced in theinteraction 120 or 152. By way of non-limiting example, the list ofbusiness entities referenced by interactions 120 and 152 may include atleast one of a policy, an account, a customer, an involved or associatedthird party, and/or other parties identified in interactions 120 and152. Interaction metadata 121 may also include, the skill type necessaryto assist the incoming interaction 120, the amount of time between anincoming interaction 120 and an outgoing interaction 152 related to theincoming interaction 120, the number of times an incoming interaction120 has been routed to a different CSR, which could be the number oftimes the current incoming interaction 120 has been routed, but couldalso include a combination of the number of times the current incominginteraction 120 has been routed along with the number of times anyprevious related incoming interactions 120 have been routed. Interactionmetadata may also include customer identification, CSR identificationand other statistical data about the customer or CSR involved in theinteraction. In certain embodiments, the intent of the correspondence isexpressed as a list of action points ordered by importance or urgency.

CEC system 100 also includes an Interaction Analysis Engine (IAE) 130having an IAE software module 131 and optional IAE storage 133. IAE 130may be a processor or a combination of a processing system and a storagesystem. IAE 130 receives incoming interactions 120 and 152 withinteraction metadata 121 from MAS unit 110 and analyzes it using IAEsoftware module 131 to assign an interaction score 142 to theinteraction 120 and 152 based on predetermined criteria 132 within IAEsoftware module 131. Optionally, IAE 130 may also permanently ortemporarily save a copy of the interaction 120 and 152, the interactionmetadata 121, and/or the interaction score 142 to internal or externalIAE storage 133.

Predetermined criteria 132 include rules conditioned on interactionmetadata 121, including, but not limited to, sentiment, red flaginformation, routing information, duration/time lapse information, CSRor CSR group availability, CSR or CSR group workload, and CSR or CSRgroup skills. Predetermined criteria 132 may be dynamically updated by aCSR or another party as any of the associated rules change. Depending oninteraction metadata 121 and predetermined criteria 132, IAE 130 willdetermine an interaction score 142 to be assigned to the interaction 120and 152. Different types of interaction metadata 121 may be weigheddifferently by the predetermined criteria 132. By way of non-limitingexample, if the sentiment of an incoming interaction 120 is neutral andthe number of times the incoming interaction has beenrerouted/transferred is high, the interaction score 142 assigned may behigher than the interaction score 142 for an incoming interaction 120that has a sentiment of mildly frustrates, but has not beenrerouted/transferred. It should be understood that the predeterminedcriteria 132 that is applied to incoming interactions 120 may bedifferent than the predetermined criteria 132 that is applied tooutgoing interactions 152. By way of non-limiting example, an outgoinginteraction 152 that has a sentiment of high frustration may have aninteraction score 142 that is lower than an incoming interaction 120that has a sentiment of high frustration.

The IAE software module 131 also compares the interaction score 142 to ainteraction quality threshold. Interactions 120 and 152 with interactionscores 142 below the interaction quality threshold simply pass to theirfinal destination without alerting a supervisor to the interaction 120and 152. Interactions 120 and 152 with interaction scores 142 above theinteraction quality threshold indicate that supervisor interaction maybe required. Such interactions 120 and 152 are still passed to theirfinal destination, but are also added, in real-time to be alerted to asupervisor. It is to be understood that the interaction 120 and 152 maybe passed simultaneously or before the analysis is complete indetermining whether it should also be alerted to a supervisor. Asupervisor can be a specific supervisor, a group of supervisors, aspecific queue, or a dynamically updated listing of problem interactionsthat is accessible by all supervisors. For example, an interaction 120and 152 that fails the interaction quality threshold may be passeddirectly to a specific supervisor that has skills in the interactiontype, may be passed to a specific supervisor who assisted on apreviously related interaction, or may be passed directly to asupervisor or a queue of supervisors that has immediate ability toreview the interaction. In another embodiment, an interaction 120 and152 that fails the interaction quality threshold may be places on anotice list for interactions needing supervision, possibly along withinformation relating to why supervision is advisable, and a supervisormay decide which interactions to monitor. It should be understood thatthese are merely examples of how notice of the problem interaction isdelivered to a supervisor or a group of supervisors. The importance ofthe notification is that it be done in real-time such that thesupervisor can assist in the interaction as soon as it is determinedthat assistance is needed. Optionally, if IAE determines to pass theinteraction 120 and 152 to a supervisor, IAE 130 may also passinteraction metadata 121 and/or interaction score 142 to the samedestination as the interaction 120 and 152.

Interaction quality thresholds may vary according to the type ofinteraction 120 and 152 and the channel on which the interaction 120 and152 occurs. For example, an email message directly replying to a writtencommunication from an important customer may have a much lowerinteraction quality threshold than a general social media post to ahigh-volume account or a minor notice posted to the CSR's companyintranet. The supervisor staff or another entity may update any of theinteraction quality thresholds as needed to ensure excellentcommunications.

Supervisors may be identified or grouped by level of authority or skill,skill set, product or service line, department, assigned customers oraccounts, prior customer interactions, any other quality, or anycombination of qualities. Queues and queue groups may be associated witha level of urgency or importance, with one or more specific issues,types of issue, products, services, product lines, service lines,customers, accounts, departments, or groups of departments, any otherquality, or any combination of qualities. Supervisor groups and queuegroups may be predetermined or created and updated dynamically to fitcurrent or anticipated needs.

In the exemplary embodiment, CEC system 100 also includes at least asecond CEC desktop 140 used by the supervisor for receiving notice ofthe problem interaction, and optionally receiving the interaction 120and 152, the interaction metadata 121, and interaction score 142. TheCEC desktop 140 is connected to the CEC system 100 including the CECphone system and textual communication system. The supervisor, alongwith the CEC desktop 140 is also connected to the CEC system interactionservers 160. The CEC desktop 140 not only allows the supervisor to benotified of the problem interaction, but also allows the supervisor totake any number of actions associated with the problem interaction,including, but not limited to, sending suggestions to the CSR to aid inthe interaction, sending an outgoing interaction 152 directly to thesender of an incoming interaction 120, if the interaction is a phoneconversation, allowing the supervisor to listen in on the interaction,allowing the supervisor to become a third-party in the interaction, andallowing the interaction to be transferred directly to the supervisor.It should be understood that how the supervisor assists in aninteraction may be determined by the supervisor through review of theinformation provided for the problem interaction and use of the CECdesktop 140 or, in embodiments, the IAS 130 may automatedly determinehow the supervisor assists in the interaction based on the interactionscore 142. CEC desktop 102 and 140 may also receive input for updatingpredetermined criteria 132 and/or the interaction quality threshold.

FIG. 2 depicts a flowchart of an exemplary embodiment of method 200 forautomatedly determining current interactions that are likely to benefitfrom supervision.

In step 202, the MAS unit receives an interaction 120 or 152. Aninteraction is either an incoming interaction 120 received from outsidethe CEC system or an outgoing interaction 152 received from inside theCEC system.

In step 204, the MAS unit performs an analysis of the interaction 120and 152 using the MAS software module. The analysis may evaluate theinteraction's content, existing attached metadata, header data, andattachments.

In step 206, the MAS unit generates interaction metadata 121 for theinteraction 120 and 152 based on the analysis of step 204.

In step 208, the MAS unit passes the interaction 120 and 152 and theassociated interaction metadata 121 to an IAE. Optionally, the MAS unitmay also pass the interaction 120 and 152 and/or associated interactionmetadata to MAS storage.

In step 210, the IAE performs an analysis of the interaction metadata121 using an IAE software module. The analysis is based on predeterminedcriteria 132 in the IAE software module.

In step 212, the IAE assigns an interaction score 142 to the interaction120 and 152 based on the analysis of step 210. Optionally, the IAE mayalso permanently or temporarily save a copy of the interaction 120 and152, the interaction metadata 121, and/or the interaction score 142 tointernal or external IAS storage.

In step 214, the IAE compares the interacts score 142 to a interactionquality threshold. The IAS software module may be programmed withmultiple different interaction quality thresholds for different types ofinteractions and different types of channels the interaction occurs.

If the interaction score is above the interaction quality threshold, instep 216, the IAE transmits, in real-time, notice of the interaction tothe supervisor. This type of interaction is considered a probleminteraction or an interaction that would benefit for supervisorintervention. In embodiments, the IAE will transmit notice of theinteraction directly to the supervisor. In embodiments, the IAE willtransmit notice of the interaction to a dynamically created list ofproblem interactions that is accessible by supervisors wherein asupervisor can select a problem interaction from the list. Of importanceis the list is created in real-time and only contains interactions thathave current interaction scores above the interaction quality threshold.

In optional step 218, the IAE also transmits the interaction 120 and152, the interaction metadata 121, and/or the interaction score 142 tothe supervisor if the interaction score is above the interaction qualitythreshold.

In optional step 220, the supervisor retrieves the notice of theinteraction from the queue or one of the queues in the queue group, orfrom the problem interaction list.

In step 222, the CEC desktop displays the notice of the interaction forsupervisor review.

In optional step 224, the CEC desktop displays the interaction, theinteraction metadata, and/or the interaction score for CSR review. Incertain embodiments, this step may occur before or simultaneously withstep 222.

If the interaction score is below the interaction quality threshold,steps 216-224 are skipped.

If the communication is complete, meaning there are no further incominginteractions 120 or outgoing interactions 152 being received in realtime, at optional step 226, the IAE receives an update to thepredetermined criteria and/or interaction quality threshold from theCSR, the supervisor or another party. In various embodiments, this stepmay occur before or after any other step in method 200.

If the communication is not complete, then the MAS receives the nextinteraction in the communication and at least steps 202-214 arerepeated.

FIG. 3 depicts an exemplary embodiment of system 300 for automatedlydetermining current interactions that are likely to benefit fromsupervision. System 300 is generally a computing system that includes aprocessing system 306, a storage system 304, software 302, acommunication interface 308, and a user interface 310. Processing system306 loads and executes software 302 from the storage system 304,including a software module 320. When executed by computing system 300,software module 320 directs the processing system 306 to operate asdescribed in herein in further detail in accordance with the method 200.

Computing system 300 includes two software modules 320 for performingthe functions of MAS software module 111 and/or IAE software module 131.Although computing system 300 as depicted in FIG. 3 includes twosoftware modules 320 in the present example, it should be understoodthat one or more modules could provide the same operation. Similarly,while the description as provided herein refers to a computing system300 and a processing system 306, it is to be recognized thatimplementations of such systems can be performed using one or moreprocessors, which may be communicatively connected, and suchimplementations are considered to be within the scope of thedescription. It is also contemplated that these components of computingsystem 300 may be operating in a number of physical locations.

The processing system 306 can comprise a microprocessor and othercircuitry that retrieves and executes software 302 from storage system304. Processing system 306 can be implemented within a single processingdevice but can also be distributed across multiple processing devices orsub-systems that cooperate in existing program instructions. Examples ofprocessing systems 306 include general purpose central processing units,application specific processors, and logic devices, as well as any othertype of processing device, combinations of processing devices, orvariations thereof.

The storage system 304 can comprise any storage media readable byprocessing system 306, and capable of storing software 302. The storagesystem 304 can include volatile and non-volatile, removable andnon-removable media implemented in any method or technology for storageof information, such as computer readable instructions, data structures,program modules, or other data. Storage system 304 can be implemented asa single storage device but may also be implemented across multiplestorage devices or sub-systems. Storage system 304 can further includeadditional elements, such a controller capable of communicating with theprocessing system 306.

Examples of storage media include random access memory, read onlymemory, magnetic discs, optical discs, flash memory, virtual memory, andnon-virtual memory, magnetic sets, magnetic tape, magnetic disc storageor other magnetic storage devices, or any other medium which can be usedto store the desired information and that may be accessed by aninstruction execution system, as well as any combination or variationthereof, or any other type of storage medium. In some implementations,the storage media can be a non-transitory storage media. In someimplementations, at least a portion of the storage media may betransitory. Storage media may be internal or external to system 300.

User interface 310 can include one or more CEC desktops 140 and 102, amouse, a keyboard, a voice input device, a touch input device forreceiving a gesture from a user, a motion input device for detectingnon-touch gestures and other motions by a user, and other comparableinput devices and associated processing elements capable of receivinguser input from a user. The user interface 310 through the CEC desktops140 and 102 is also integrated into the CEC system 100 allowing the userto access the CEC telephone system and the CEC text communicationssystems. Output devices such as a video display or graphical display candisplay notice of problem interactions, incoming interactions 120,outgoing interactions 152, interaction metadata 121, interaction score142, CEC desktop 140 and 102, or another interface further associatedwith embodiments of the system and method as disclosed herein. Speakers,printers, haptic devices and other types of output devices may also beincluded in the user interface 310. A CSR, a supervisor or other staffcan communicate with computing system 300 through the user interface 310in order to view notice of problem interactions, incoming interactions120, outgoing interactions 152, interaction metadata 121, interactionscore 142, and in order to update predetermined criteria 132,interaction quality thresholds, manage an interaction, or any number ofother tasks the CSR, supervisor or other staff may want to complete withcomputing system 300.

As described in further detail herein, computing system 300 receives andtransmits data through communication interface 308. In embodiments, thecommunication interface 308 operates to send and/or receive data, suchas, but not limited to, incoming interactions 120 and outgoinginteractions 152 to/from other devices and/or systems to which computingsystem 300 is communicatively connected, and to receive and processclient input, as described in greater detail above. The client input caninclude incoming interactions 120, details about a request, work orderor other set of information that will necessitate an interaction betweenthe client and the agent. Client input may also be made directly to theCSR, as described in further detail above.

In the foregoing description, certain terms have been used for brevity,clearness, and understanding. No unnecessary limitations are to beinferred therefrom beyond the requirement of the prior art because suchterms are used for descriptive purposes and are intended to be broadlyconstrued. The different configurations, systems, and method stepsdescribed herein may be used alone or in combination with otherconfigurations, systems and method steps. It is to be expected thatvarious equivalents, alternatives and modifications are possible withinthe scope of the appended claims.

What is claimed is:
 1. An automated computerized method for determiningif an interaction is likely to require supervisor assistance,comprising: providing a customer engagement center for receiving aplurality of interactions and determining if any of the plurality ofinteractions is likely to require supervisor assistance, wherein thecustomer engagement center includes a metadata analytics service unitand an interaction analysis engine; receiving at least one interactionof the plurality of interactions; performing a metadata analysis of theinteraction to generate interaction metadata for the receivedinteraction, wherein the interaction metadata includes a sentiment ofthe interaction and at least one additional metadata parameter;generating interaction metadata for the interaction based on themetadata analysis; performing an interaction analysis of the interactionmetadata to determine an interaction score for the interaction;comparing the interaction score to an interaction quality threshold; andpassing a notice of the interaction to a supervisor representative ifthe interaction score is higher than the interaction quality threshold.2. The method of claim 1, wherein the at least one additional metadataparameter is one of a type of interaction for the interaction, durationof time between the interaction and a previous related interaction or anumber of times the interaction has been transferred.
 3. The method ofclaim 1, wherein the supervisor representative is at least one of anindividual supervisor, a supervisor group, a queue, or a probleminteraction list.
 4. The method of claim 1, the method furthercomprising displaying the notice of the interaction on a CEC desktop. 5.The method of claim 4, wherein the CEC desktop displays as least one ofthe interaction metadata, the interaction score, or the interaction. 6.An automated computer system for determining if an interaction is likelyto require supervisor assistance, comprising: a customer engagementcenter for receiving a plurality of interactions and determining if anyof the plurality of interactions is likely to require supervisorassistance, the customer engagement center including: a metadataanalytics service unit; an interaction analysis engine; a processor; anda non-transitory computer readable medium programmed with computerreadable code that upon execution by the processor causes the processorto: provide a customer engagement center for receiving a plurality ofinteractions and determining if any of the plurality of interactions islikely to require supervisor assistance, wherein the customer engagementcenter includes a metadata analytics service unit and an interactionanalysis engine, receive at least one interaction of the plurality ofinteractions, perform a metadata analysis of the interaction to generateinteraction metadata for the received interaction, wherein theinteraction metadata includes a sentiment of the interaction and atleast one additional metadata parameter, generate interaction metadatafor the interaction based on the metadata analysis, perform aninteraction analysis of the interaction metadata to determine aninteraction score for the interaction, compare the interaction score toan interaction quality threshold, and pass a notice of the interactionto a supervisor representative if the interaction score is higher thanthe interaction quality threshold.
 7. The system of claim 6, wherein theat least one additional metadata parameter is one of a type ofinteraction for the interaction, duration of time between theinteraction and a previous related interaction or a number of times theinteraction has been transferred.
 8. The system of claim 6, wherein thesupervisor representative is at least one of an individual supervisor, asupervisor group, a queue, or a problem interaction list.
 9. The systemof claim 6, wherein the processor is further instructed to display thenotice of the interaction on a CEC desktop.
 10. The system of claim 9,wherein the CEC desktop displays as least one of the interactionmetadata, the interaction score, or the interaction.
 11. An automatedcomputerized method for determining if a customer service conversationis likely to require supervisor assistance, comprising: providing acustomer engagement center for receiving a plurality of ongoing customerservice conversations and determining if any of the plurality ofcustomer service conversations are likely to require supervisorassistance, wherein the customer engagement center includes a metadataanalytics service (MAS) unit and an interaction analysis engine (IAE);continually monitoring an ongoing customer service conversation with theMAS unit to determine if the ongoing customer service conversationshould be sent to a supervisor representative, wherein the ongoingcustomer service conversation includes a plurality of incominginteractions and a plurality of outgoing interactions; generating, bythe MAS, interaction metadata for the ongoing customer serviceconversation based on a metadata an analysis of the ongoing customerservice conversation, wherein the interaction metadata includes asentiment of the interaction and at least one additional metadataparameter; passing the interaction metadata to the IAE; performing aninteraction analysis of the interaction metadata using an IAE softwaremodule to determine an interaction score for the interaction;determining an interaction score for the interaction based on theinteraction analysis of the IAE software module; comparing theinteraction score to an interaction quality threshold; transmitting anotice of the ongoing customer service conversation to the supervisorrepresentative if the interaction score is higher than the interactionquality threshold; and repeating the interaction metadata generation,interaction analysis, interaction score determination, comparison andtransmission steps until the ongoing customer service conversation iscompleted.
 12. The method of claim 11, wherein the at least oneadditional metadata parameter is one of a type of interaction for theinteraction, duration of time between the interaction and a previousrelated interaction or a number of times the interaction has beentransferred.
 13. The method of claim 11, wherein the supervisorrepresentative is at least one of an individual supervisor, a supervisorgroup, a queue, or a problem interaction list.
 14. The method of claim11, the method further comprising displaying the notice of theinteraction on a CEC desktop.
 15. The method of claim 14, wherein theCEC desktop displays as least one of the interaction metadata, theinteraction score, or the interaction.
 16. An automated computer systemfor determining if an ongoing customer service conversation is likely torequire supervisor assistance, comprising: a customer engagement centerfor receiving a plurality of ongoing customer service conversations anddetermining if any of the plurality of customer service conversationsare likely to require supervisor assistance: a metadata analyticsservice (MAS) unit; an interaction analysis engine (IAE); a processor;and a non-transitory computer readable medium programmed with computerreadable code that upon execution by the processor causes the processorto: continually monitor an ongoing customer service conversation withthe MAS unit to determine if the ongoing customer service conversationshould be sent to a supervisor representative, wherein the ongoingcustomer service conversation includes a plurality of incominginteractions and a plurality of outgoing interactions, generate, by theMAS, interaction metadata for the ongoing customer service conversationbased on a metadata an analysis of the ongoing customer serviceconversation, wherein the interaction metadata includes a sentiment ofthe interaction and at least one additional metadata parameter, pass theinteraction metadata to the IAE, perform an interaction analysis of theinteraction metadata using an IAE software module to determine aninteraction score for the interaction, determine an interaction scorefor the interaction based on the interaction analysis of the IAEsoftware module, compare the interaction score to an interaction qualitythreshold, transmit a notice of the ongoing customer serviceconversation to the supervisor representative if the interaction scoreis higher than the interaction quality threshold, and repeat theinteraction metadata generation, interaction analysis, interaction scoredetermination, comparison and transmission steps until the ongoingcustomer service conversation is completed.
 17. The system of claim 16,wherein the at least one additional metadata parameter is one of a typeof interaction for the interaction, duration of time between theinteraction and a previous related interaction or a number of times theinteraction has been transferred.
 18. The system of claim 16, whereinthe supervisor representative is at least one of an individualsupervisor, a supervisor group, a queue, or a problem interaction list.19. The system of claim 16, wherein the processor is further instructedto display the notice of the interaction on a CEC desktop.
 20. Thesystem of claim 19, wherein the CEC desktop displays as least one of theinteraction metadata, the interaction score, or the interaction.